Cross In-Database Machine Learning

2. The computer-implemented method of claim 1, wherein the binary file comprises model parameters of the trained machine learning model.

3. The computer-implemented method of claim 2, wherein the model parameters of the trained machine learning model comprise weights for a neural network model, coefficients for a linear regression model, or rules for a decision tree model.

4. The computer-implemented method of claim 1, wherein the binary file comprises an identification of a machine learning algorithm.

5. The computer-implemented method of claim 1, wherein the binary file comprises one or more hyperparameters of the trained machine learning model.

6. The computer-implemented method of claim 1, further comprising storing the binary file in a persistence layer of an application platform, wherein the recreating of the trained machine learning model comprises accessing the stored binary file.

8. The computer-implemented method of claim 7, wherein the function comprises causing the generated inference result to be displayed on the computing device.

10. The system of claim 9, wherein the binary file comprises model parameters of the trained machine learning model.

11. The system of claim 10, wherein the model parameters of the trained machine learning model comprise weights for a neural network model, coefficients for a linear regression model, or rules for a decision tree model.

12. The system of claim 9, wherein the binary file comprises an identification of a machine learning algorithm.

13. The system of claim 9, wherein the binary file comprises one or more hyperparameters of the trained machine learning model.

16. The system of claim 15, wherein the function comprises causing the generated inference result to be displayed on the computing device.

18. The non-transitory machine-readable storage medium of claim 17, wherein the binary file comprises model parameters of the trained machine learning model.

19. The non-transitory machine-readable storage medium of claim 18, wherein the model parameters of the trained machine learning model comprise weights for a neural network model, coefficients for a linear regression model, or rules for a decision tree model.

20. The non-transitory machine-readable storage medium of claim 17, wherein the binary file comprises an identification of a machine learning algorithm.